<![CDATA[This study aims to evaluate and compare the performance of five Machine Learning classification algorithms—Support Vector Machine (SVM), Neural Network (NN), Logistic Regression (LR), Decision Tree (DT), and K-Nearest Neighbors (KNN)—in detecting breast cancer using the Breast Cancer Wisconsin (Diagnostic) dataset. The evaluation was conducted in stages, beginning with baseline model training, followed by optimization using Bagging ensemble techniques, and further enhanced with the Adaboost algorithm. Model performance was assessed using accuracy, confusion matrix, ROC curve, and Area Under Curve (AUC) metrics. The results show that Logistic Regression demonstrated the most consistent performance, achieving 97.1% accuracy and the highest AUC of 99.7% after Adaboost was applied. Decision Tree also showed noticeable improvement in both accuracy and AUC. In contrast, Neural Network and KNN models were found incompatible with Adaboost. These findings highlight that the effectiveness of ensemble techniques is highly dependent on the nature of the base algorithm. This research contributes to a better understanding of how to select and combine classification algorithms with appropriate optimization strategies to improve the accuracy of breast cancer diagnosis. ]]>
Copyrights © 2025
